As discussions surrounding the CLARITY Act—often referred to as the crypto market structure bill—continue in Washington, Kristin Smith, President of the Solana Policy Institute, has provided insights on the current status of the legislation and the organization’s top priorities.
Solana Policy Institute’s Optimism For CLARITY Act
One of the main priorities disclosed by Smith in a recent post on social media platform X (formerly Twitter), is the importance of protecting open-source developers in the legislative landscape.
Smith pointed out that the recent delay in the markup of the market structure bill last week after Coinbase’s withdrawal should be seen as a temporary setback. “Despite the delay, industry engagement remains robust, and there is clear bipartisan support to achieve durable regulatory clarity for market structure,” she noted.
The Senate Agriculture Committee is making advancements with its own draft of the legislation expected to be released on Wednesday, as earlier reported by Bitcoinist.
Smith also highlighted a shared objective: to create a framework that protects consumers, fosters innovation, and provides certainty for developers operating in the United States. A central tenet of this goal is the safeguarding of developers, which Smith argued is crucial for the success of the industry.
Smith Advocates For Developer Protections
The Solana Institute was founded to ensure that policymakers gain a comprehensive understanding of public blockchains and the protocols that underpin them.
Smith articulated the critical role that open-source software plays within the crypto ecosystem, noting that developers around the world collaborate to produce software that anyone can inspect, use, or improve. “Openness is a strength—not a liability,” she asserted.
However, she raised concerns regarding the case against Roman Storm of Tornado Cash, indicating that it treats open-source innovation as something questionable. Smith warned that penalizing developers merely for writing and publishing open-source code endangers all those involved in such collaborative efforts.
She emphasized the “chilling effect” that the prosecution could have on open-source developers, asserting that writing code is an expressive act protected by the First Amendment.
Smith called for clear policy that differentiates between bad actors and developers working on lawful, general-purpose tools. To bolster this cause, she encouraged supporters to draft letters expressing their stance in favor of open-source protections.
Roman Storm responded to Smith’s support, thanking her and the broader community for advocating for open-source principles. He remarked, “Criminalizing the act of writing and publishing code threatens not just one developer, but the foundations of digital security, privacy, and innovation.”
At the time of writing, Solana’s native token, SOL, was trading at $130.33, mirroring the performance of the broader crypto market, dropping 11% in the weekly time frame.
Featured image from DALL-E, chart from TradingView.com
Setapp Mobile will shut down in February, citing Apple’s complex EU terms as developers weigh new fees, link-out rules, and uncertain alternative stores.
Setapp Mobile will shut down in February, citing Apple’s complex EU terms as developers weigh new fees, link-out rules, and uncertain alternative stores.
Machine learning has officially moved out of the lab.
In 2026, businesses are no longer asking “Can we build an ML model?” — they’re asking “Can we run reliable, scalable, and cost-efficient ML pipelines in production?”
The difference between experimental ML and real business impact lies in production-grade ML pipelines. These pipelines ingest data, train models, deploy them, monitor performance, retrain automatically, and integrate with real-world systems. And at the center of all this complexity is one critical decision:
TensorFlow remains one of the most trusted and widely adopted frameworks for building end-to-end ML systems. But in 2026, simply knowing TensorFlow APIs is not enough. Companies need TensorFlow developers who can design, deploy, optimize, and maintain production ML pipelines that actually work at scale.
In this guide, we’ll explore why production ML pipelines matter, why TensorFlow is still a leading choice, what skills modern TensorFlow developers must have, and how hiring the right talent determines long-term ML success.
Why Production ML Pipelines Matter More Than Models
Many organizations still equate ML success with model accuracy. In reality, accuracy is only one small part of the equation.
A production ML pipeline must handle:
continuous data ingestion
feature engineering at scale
automated training and validation
safe deployment and rollback
monitoring and alerting
retraining and versioning
integration with business systems
Without these capabilities, even the best-performing model becomes unusable.
This is why organizations that succeed with ML focus less on individual models and more on robust ML pipelines — and why they deliberately hire TensorFlow developers with production experience.
Why TensorFlow Remains a Top Choice for Production ML in 2026
Despite the growth of alternative frameworks, TensorFlow continues to dominate production ML environments for several reasons.
1. End-to-End ML Ecosystem
TensorFlow supports the full ML lifecycle — from data pipelines and training to deployment and monitoring.
2. Proven Scalability
TensorFlow is battle-tested at scale, supporting distributed training, GPUs, TPUs, and large enterprise workloads.
3. Production-Ready Tooling
With tools like TensorFlow Serving, TensorFlow Extended (TFX), and TensorFlow Lite, teams can deploy models reliably across environments.
4. Enterprise Trust
Many enterprises rely on TensorFlow due to its stability, long-term support, and strong community.
Because of this maturity, companies building serious ML systems continue to hire TensorFlow developers for production pipelines.
Why Production ML Pipelines Fail Without the Right Developers
Production ML is hard — and it fails more often than most teams expect.
Common failure points include:
brittle data pipelines
inconsistent feature engineering
manual training processes
deployment bottlenecks
lack of monitoring
no retraining strategy
poor collaboration between ML and DevOps
These problems rarely come from the framework itself. They come from lack of production ML expertise.
Hiring TensorFlow developers with hands-on pipeline experience dramatically reduces these risks.
What Makes a Production ML Pipeline “Production-Ready”?
Before discussing hiring, it’s important to define what production-ready actually means.
A mature ML pipeline in 2026 should be:
Automated: minimal manual intervention
Scalable: handles growing data and traffic
Observable: monitored, logged, and auditable
Resilient: supports rollback and recovery
Cost-Efficient: optimized for compute and storage
Maintainable: easy to update and extend
TensorFlow developers play a key role in delivering all of these qualities.
The Role of TensorFlow Developers in Production ML Pipelines
When you hire TensorFlow developers for production ML, you’re not just hiring model builders — you’re hiring system engineers.
Here’s what experienced TensorFlow developers contribute.
1. Designing Scalable Data Pipelines
Data is the foundation of ML.
TensorFlow developers design pipelines that:
ingest data from multiple sources
validate and clean inputs
handle missing or noisy data
scale with volume and velocity
Poor data pipelines are the number one cause of ML failures.
2. Building Consistent Feature Engineering Workflows
Feature consistency is critical.
TensorFlow developers ensure:
training and inference use identical features
feature logic is versioned and reproducible
transformations are efficient and scalable
This consistency prevents subtle bugs that degrade model performance.
3. Training Models at Scale
Production ML often requires large datasets and complex models.
TensorFlow developers handle:
distributed training
GPU/TPU optimization
memory management
experiment tracking
This ensures training is efficient, repeatable, and cost-controlled.
4. Model Evaluation and Validation
Before deployment, models must be validated rigorously.
TensorFlow developers implement:
automated evaluation pipelines
performance thresholds
bias and drift checks
comparison with previous versions
This protects production systems from regressions.
5. Deployment and Serving
Model deployment is where many teams struggle.
TensorFlow developers design serving systems that:
support real-time and batch inference
scale horizontally
manage versions and rollbacks
meet latency requirements
This is essential for production reliability.
6. Monitoring and Observability
Once deployed, models must be watched continuously.
TensorFlow developers build monitoring for:
prediction quality
data drift
performance degradation
system health
Without monitoring, production ML becomes a blind spot.
7. Automated Retraining and CI/CD for ML
In 2026, ML pipelines must evolve automatically.
TensorFlow developers implement:
retraining triggers
CI/CD pipelines for models
automated testing and validation
safe promotion to production
This keeps ML systems accurate over time.
Key Skills to Look for When You Hire TensorFlow Developers in 2026
Hiring the right TensorFlow developers requires evaluating the right skill set.
1. Deep TensorFlow Framework Knowledge
Developers should be fluent in:
TensorFlow 2.x
Keras and low-level APIs
custom training loops
This enables flexibility and optimization.
2. Production ML and MLOps Experience
Look for experience with:
ML pipelines
CI/CD for ML
model versioning
monitoring and retraining
Production ML experience is non-negotiable.
3. Distributed Systems and Scalability
TensorFlow developers must understand:
distributed training
parallel data processing
resource management
Scalability is critical in production environments.
4. Cloud and Infrastructure Familiarity
Production ML often runs in the cloud.
Developers should know how to:
deploy TensorFlow models in cloud environments
optimize compute usage
manage storage and networking
5. Performance and Cost Optimization
Unoptimized ML pipelines can be expensive.
TensorFlow developers should optimize:
training time
inference latency
resource utilization
This directly impacts ROI.
6. Software Engineering Best Practices
Production ML is software engineering.
Developers must follow
clean architecture
testing and documentation
version control
This ensures long-term maintainability.
Common Hiring Mistakes in Production ML Projects
Many organizations make avoidable mistakes, such as:
hiring researchers instead of production engineers
focusing only on model accuracy
ignoring pipeline automation
underestimating monitoring needs
skipping MLOps expertise
Avoiding these mistakes starts with hiring the right TensorFlow developers.
How to Evaluate TensorFlow Developers for Production Pipelines
To assess candidates effectively:
ask about real production ML systems
discuss pipeline failures and lessons learned
review deployment and monitoring strategies
evaluate system design thinking
Practical experience matters more than theoretical knowledge.
Hiring Models for TensorFlow Developers in 2026
Organizations use different hiring models based on needs.
In-House TensorFlow Teams
Best for long-term, core ML platforms.
Dedicated Remote TensorFlow Developers
Popular for flexibility, cost efficiency, and speed.
Project-Based Engagements
Useful for pipeline audits or migrations.
Many companies choose dedicated models to scale faster.
Why Businesses Choose to Hire TensorFlow Developers Through Partners
The demand for TensorFlow talent is high.
Working with specialized partners offers:
access to experienced developers
faster onboarding
reduced hiring risk
flexible scaling
This approach accelerates production ML adoption.
Why WebClues Infotech Is a Trusted Partner to Hire TensorFlow Developers
WebClues Infotech helps organizations build production-ready ML pipelines by providing skilled TensorFlow developers with real-world experience.
Their TensorFlow experts offer:
end-to-end ML pipeline expertise
production deployment experience
MLOps and automation skills
scalable engagement models
If you’re planning to hire TensorFlow developers for production ML pipelines in 2026.
Industries Benefiting Most From Production ML Pipelines
In 2026, production ML pipelines are driving value across:
fintech and fraud detection
healthcare analytics
retail personalization
logistics and demand forecasting
SaaS intelligence
manufacturing optimization
Across industries, success depends on pipeline reliability.
The ROI of Hiring the Right TensorFlow Developers
While experienced TensorFlow developers require investment, they deliver:
faster time to production
fewer outages and failures
lower long-term costs
higher trust in ML systems
The ROI compounds as pipelines scale.
Future Trends in Production ML Pipelines
Looking ahead, production ML pipelines will emphasize:
automation over manual processes
tighter integration with business systems
stronger governance and compliance
cost-aware ML operations
TensorFlow developers who understand these trends will remain in high demand.
Conclusion: Production ML Success Starts With Hiring the Right TensorFlow Developers
In 2026, ML success is no longer defined by experimentation — it’s defined by production reliability.
Organizations that invest in strong ML pipelines gain a lasting competitive advantage. And those pipelines are built by people, not frameworks.
By choosing to hire TensorFlow developers with proven production ML experience, businesses ensure their models don’t just work in theory — but deliver real, measurable value in the real world.
If your goal is to build scalable, reliable, and future-proof ML systems, the smartest move you can make is to hire the right TensorFlow developers today.
The recently released draft of the CLARITY Act, a significant piece of legislation aimed at regulating the crypto market, has ignited a wave of criticism from supporters within the community.
Initially, the bill was meant to include protections for developers. However, expert commentary suggests that it opens the door to continued prosecution of developers and enhances surveillance measures for users of non-custodial software.
Crypto Market Structure Bill Draft Lacks Essential Protections
Market expert Ryan Adams highlighted another key issue in the crypto bill, stating that if banks succeed in eliminating stablecoin yield provisions within the CLARITY Act, it would indicate that the Senate is prioritizing bank interests over those of the general public.
Adams’s concerns were echoed by various users, who opined that the strategy appears orchestrated to allow banks to benefit by controlling how yields are managed and distributed.
An independent report by The Rage reinforces these worries, detailing how the proposed draft includes so-called developer protections that may fall short. Notably absent are safeguards against the rigorous implications of the Bank Secrecy Act (BSA) for self-custodial wallets.
Additionally, the draft hints at possible applications to decentralized finance (DeFi) that could empower agencies to implement Travel Rule-like regulations, along with anti-money laundering (AML) measures targeting web-based interfaces and blockchain analysis firms.
Per the report, the Senate has already received 137 amendments to the draft ahead of its markup, scheduled for January 15. A revised version of the Blockchain Regulatory Certainty Act (BRCA) is also included, which has been seen as vital for protecting developers.
BRCA Loopholes
While the BRCA offers exemptions under AML and counter-terrorist financing regulations, it continues to leave developers vulnerable to accountability for the actions of users utilizing their software.
The BRCA states that “non-controlling” developers—defined as those without unilateral control over digital asset transactions—will not be categorized as money transmitters under the relevant laws. However, this only alleviates certain charges and doesn’t prevent criminal liability for those whose software is misused.
Pro-crypto Senator Cynthia Lummis remarked on this aspect of the BRCA, indicating that it retains all necessary AML protections, which implies that despite any positives, accountability remains a looming threat for developers.
Simultaneously, the “Keep Your Coins Act” within the draft includes provisions claiming that federal agencies cannot prohibit self-custody of digital assets. However, further stipulations assert that this right does not prevent the application of laws concerning illicit finance, leaving loopholes for government intervention.
The Securities and Exchange Commission’s (SEC) past attempts to impose a broker rule that would classify decentralized finance services as intermediaries requiring reporting obligations have been echoed in the current draft.
This time, the Senate Banking Committee appears to be leaning towards a similar regulatory approach, aiming to provide guidance on BSA and AML compliance for “non-decentralized finance protocols,” thereby raising concerns about the implications for crypto developers who maintain and update protocols.
Privacy Concerns Mount
Under the new sections, the Senate Banking Committee introduces a concept termed “Distributed Ledger Application Layers,” which the report claims invites scrutiny and creates compliance obligations for software applications that allow users to interact with decentralized finance protocols.
The provisions also compel the Treasury to develop additional oversight mechanisms to mitigate exposure to illicit financing risks identified through distributed ledger analysis tools, effectively ensuring that crypto transactions remain under close scrutiny.
As it currently stands, the lack of robust protections for developers and users involved in privacy-enhancing technologies in this current draft suggests that the Senate’s proposal for market structure will do little to safeguard non-custodial developers.
Instead, it further entrenches their vulnerability to government oversight and user surveillance. Ultimately, these developments present a significant challenge for privacy software users and developers.
Featured image from DALL-E, chart from TradingView.com
US Senators Cynthia Lummis and Ron Wyden introduced a standalone measure that would protect blockchain developers and other non-custodial infrastructure providers from being treated as money transmitters solely for writing code or maintaining networks. The bill is being filed as the Blockchain Regulatory Certainty Act, a name that also appears in earlier House paperwork filed last year.
Crypto: Bill Aims To Protect Non-Custodial Developers
The draft would create a safe harbor for developers who do not control user funds, making liability turn on actual custody or control of assets rather than on the act of creating software. That change would mean node operators, protocol maintainers, and many open-source coders could avoid money-transmitter rules so long as they do not hold or direct users’ tokens.
Writing code is not the same as controlling money and developers who build blockchain infrastructure without touching user funds shouldn’t be treated like banks. @RonWyden and I are ensuring that won’t happen. pic.twitter.com/9zIgh07e0b
Reports have disclosed months of lobbying from exchanges, developer groups, and advocacy coalitions that urged lawmakers to clarify this point. Those groups warned that without clear language, developers could face licensing and enforcement risks that would chill US-based development. The House version of the measure first appeared in May last year and set out similar safe-harbor text.
Senate Markup Delayed As Negotiations Continue
Lawmakers have paused a larger Senate market-structure push while they work through a range of open issues, including stablecoin policy and yield rules. With that broader package pushed later into the month, sponsors moved the developer protections into a standalone bill to give that issue its own spotlight. Reports suggests the pause means Congress may act on the developer language sooner than the full market bill.
What Developers And Advocates Are Saying
Some protocol teams and industry lawyers welcomed the step as a much-needed clarification, saying it would reduce legal uncertainty for projects that do not custody funds.
Others urged care, noting that clear definitions will be crucial to prevent loopholes and to make sure bad actors cannot hide behind the safe harbor. Coverage indicates sponsors emphasized the bill’s goal is narrow: protect those who build and maintain, not those who handle other people’s assets.
The proposal for a separate law is being introduced while there are still many uncertainties surrounding how cryptocurrencies will be regulated in the US. In the latter part of 2025 and into 2026, the crypto sector has demonstrated that it has a great deal of clout within political circles in Washington D.C.
There has been a significant increase in lobbying by large crypto-related businesses as legislators review various options for regulating this industry. Several reports have linked the current political environment to the legislative actions taken to regulate crypto in Congress, as well as how interest in legislative action has increased due to Trump’s administration.
Featured image from Unsplash, chart from TradingView
Enterprise AI has entered a new phase. In 2026, organizations are no longer experimenting with generative AI in isolation — they are embedding it deeply into core systems, workflows, and decision-making processes. At the heart of this transformation are OpenAI-powered solutions: custom GPT applications, intelligent copilots, workflow automation engines, and AI agents integrated across departments.
But as adoption grows, so does complexity.
Building enterprise-grade AI solutions with OpenAI models is no longer about simple API calls or prompt demos. It requires a specialized, multidisciplinary skill set — one that blends AI engineering, software architecture, security, cost optimization, and business alignment.
That’s why organizations that want reliable, scalable results deliberately choose to hire OpenAI developers with proven enterprise experience.
In this in-depth guide, we’ll break down the top skills OpenAI developers must have in 2026 enterprise projects, why these skills matter, and how businesses can identify the right talent to turn AI ambition into operational success.
Why Enterprise OpenAI Projects Demand a New Skill Standard
Early generative AI projects focused on:
chatbots
content generation
basic internal tools
In contrast, 2026 enterprise projects involve:
proprietary data integration
multi-step workflows
AI agents that take actions
governance and compliance
cost and performance constraints
global scalability
The stakes are higher, and so is the required expertise.
Enterprises that hire general AI developers without these specialized skills often face:
hallucinations and unreliable outputs
security and data leakage risks
runaway API costs
brittle integrations
poor adoption by internal teams
This is why the decision to hire OpenAI developers must be strategic — not tactical.
What Defines an OpenAI Developer in 2026?
An OpenAI developer in 2026 is not just someone who “knows GPT.”
They are professionals who can:
design AI-powered systems end-to-end
integrate OpenAI models with enterprise platforms
control cost, latency, and risk
ensure explainability and trust
scale solutions across teams and regions
Let’s explore the skills that make this possible.
Skill #1: Deep OpenAI API and Model Expertise
This is the foundation.
Enterprise OpenAI developers must have hands-on experience with:
GPT models (text, multimodal, and tool-enabled)
embeddings and semantic search
function calling and tool usage
rate limits, quotas, and error handling
model selection based on task, cost, and latency
They understand when and how to use specific OpenAI models, rather than defaulting to the most powerful (and expensive) option.
This depth of knowledge is essential for building efficient enterprise systems.
Skill #2: Advanced Prompt Engineering and Prompt Architecture
Prompting in enterprise projects is no longer ad hoc.
OpenAI developers must design prompts that are:
structured and modular
reusable across workflows
testable and version-controlled
resistant to prompt injection
aligned with business rules
They often build prompt architectures, not single prompts — ensuring consistency, reliability, and maintainability.
This is one of the biggest differentiators when companies hire OpenAI developers for serious projects.
Skill #3: Retrieval-Augmented Generation (RAG) System Design
Enterprise AI must be grounded in real data.
OpenAI developers need strong expertise in RAG, including:
document ingestion and preprocessing
chunking strategies
embedding generation
vector database integration
relevance ranking and filtering
context window optimization
Poor RAG design leads to hallucinations, misinformation, and loss of trust. Skilled developers avoid these pitfalls.
Skill #4: LangChain and AI Workflow Orchestration
Modern OpenAI solutions rarely involve a single model call.
OpenAI developers should be proficient with frameworks like LangChain to:
orchestrate multi-step workflows
manage memory and state
integrate tools and APIs
build AI agents
handle failures gracefully
This orchestration skill is essential for enterprise automation and decision systems.
In 2026, OpenAI solutions are software products, not experiments.
Developers must follow:
clean architecture principles
modular system design
version control and CI/CD
testing and validation strategies
documentation standards
This ensures AI systems are maintainable, auditable, and scalable over time.
Skill #6: Security, Privacy, and Compliance Awareness
Enterprise AI projects deal with sensitive data.
OpenAI developers must understand:
data access controls
role-based permissions
prompt and output sanitization
secure API handling
audit logging
compliance requirements (industry-specific)
Security is not optional — it’s a core competency.
Skill #7: Cost Optimization and Token Efficiency
Unoptimized OpenAI usage can become expensive very quickly.
Skilled OpenAI developers know how to:
minimize prompt length
reuse context intelligently
cache responses
select cost-effective models
balance accuracy vs. expense
This cost discipline is critical for enterprise-scale deployments.
Skill #8: Performance and Latency Optimization
Enterprise users expect fast, reliable AI systems.
OpenAI developers must optimize:
response times
concurrency handling
batching strategies
fallback mechanisms
Latency optimization directly impacts adoption and user satisfaction.
Skill #9: Integration With Enterprise Systems
OpenAI solutions must work within existing ecosystems.
Developers need experience integrating with:
CRM and ERP platforms
document management systems
analytics tools
internal APIs and microservices
Seamless integration ensures AI delivers value where teams already work.
Skill #10: AI Agents and Autonomous Systems Design
AI agents are becoming mainstream in enterprise environments.
OpenAI developers must understand:
agent decision logic
tool selection and sequencing
validation and safety checks
human-in-the-loop escalation
This skill transforms AI from a passive assistant into an active collaborator.
Skill #11: Monitoring, Observability, and Governance
Enterprise AI systems must be observable.
OpenAI developers implement:
logging and tracing
output monitoring
performance metrics
usage analytics
governance controls
This ensures reliability, accountability, and continuous improvement.
Skill #12: Business and Domain Understanding
The best OpenAI developers understand why a system exists — not just how it works.
They can:
translate business goals into AI workflows
align outputs with KPIs
communicate trade-offs clearly
adapt solutions to industry context
This alignment is critical for enterprise success.
Skill #13: Communication and Cross-Functional Collaboration
Enterprise OpenAI projects involve many stakeholders.
Developers must communicate effectively with:
product managers
engineering teams
compliance and security
leadership
Clear communication prevents misalignment and accelerates delivery.
Common Skill Gaps to Watch Out For
When evaluating candidates, be cautious of:
prompt-only experience without system design
lack of production deployment history
no understanding of cost control
weak security awareness
inability to explain past trade-offs
These gaps often lead to fragile or expensive AI solutions.
How to Evaluate OpenAI Developers for Enterprise Projects
Effective evaluation goes beyond interviews.
Consider:
discussing real-world OpenAI projects
reviewing system architecture decisions
asking about failures and lessons learned
running small pilot engagements
This reveals true enterprise readiness.
Why Companies Prefer Dedicated OpenAI Developers in 2026
Given the demand and complexity, many organizations choose to:
hire dedicated OpenAI developers
work with specialized AI partners
scale teams flexibly
This approach reduces risk and speeds up delivery — especially for long-term initiatives.
Why WebClues Infotech Is a Trusted Partner to Hire OpenAI Developers
WebClues Infotech helps enterprises build production-ready OpenAI solutions by providing experienced OpenAI developers with strong enterprise backgrounds.
Their OpenAI talent offers:
deep GPT and OpenAI API expertise
LangChain and RAG specialization
enterprise integration experience
security and cost optimization focus
flexible hiring and engagement models
If you’re planning to hire OpenAI developers for enterprise projects in 2026.
Best Practices for Hiring OpenAI Developers in 2026
To maximize success:
define clear enterprise use cases
prioritize production experience
assess cost and security awareness
favor system thinkers over prompt demos
plan for long-term ownership
These practices help ensure AI delivers sustained value.
The Strategic Value of Hiring the Right OpenAI Developers
OpenAI technology evolves rapidly — but enterprise value comes from how well it’s engineered.
By choosing to hire OpenAI developers with the right skills, organizations gain:
reliable AI systems
predictable costs
faster time-to-value
higher trust and adoption
scalable competitive advantage
In 2026, this expertise is no longer optional — it’s mission-critical.
Conclusion: Enterprise AI Success Starts With Skilled OpenAI Developers
Generative AI is reshaping enterprise operations — but success depends on people, not just platforms.
The most impactful organizations in 2026 are those that invest in skilled OpenAI developers who can design, deploy, and govern AI systems responsibly and effectively.
If your goal is to move beyond experiments and build enterprise-grade AI solutions, the smartest move you can make is to hire OpenAI developers with the skills outlined in this guide.
VS Code developers beware: ReversingLabs found 19 malicious extensions hiding trojans inside a popular dependency, disguising the final malware payload as a standard PNG image file.
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